Genetic algorithms development for multiobjective design optimization of compressor cascade

INTRODUCTIONTurbomaChinery,especiallycompressorcascade,optimumdesignpresentsagrandchallengetonumericaloptimization.Thegoalofcompressorcascadedesignistoproducethehighestpressurerisewiththelowesttotalpressurelossattheconstantflowcondition.Parameterofpr...     

Genetic algorithms applied to turbine extraction optimization of a pressurized-water reactor

In this work, we propose the use of a genetic algorithm (GA) for the determination of the optimal fraction of mass flow rate to be extracted from each stage of the turbines of a typical pressurized-water reactor (PWR) secondary side, in order to increase cycle efficiency. Here, we show some preliminary results obtained in a case study in which the PEPSE® system was used as simulation tool.     

Modeling daylight on inclined surfaces for applications to daylight conscious architecture

In the present work the circumsolar and the point source version of the Perez model have been evaluated for South facing vertical surfaces at Madrid. Different data sets have been used. The coefficients Fij in the model have been determined using jointly experimental data obtained for vertical planes facing N, E, S and W, and also with data for just the South vertical planes. Different values of the half angle α corresponding to the circumsolar region have been used, and the most simple version of the model is recommended.     

Evolutionary algorithms approach for integrated bioenergy supply chains optimization

In this paper, we propose an optimization model and solution approach for designing and evaluating integrated system of bioenergy production supply chains, SC, at the local level. Designing SC that simultaneously utilize a set of bio-resources together is a complicated task, considered here. The complication arises from the different nature and sources of bio-resources used in bioenergy production i.e., wet, dry or agriculture, industrial etc. Moreover, the different concerns that decision makers should take into account, to overcome the tradeoff anxieties of the socialists and investors, i.e., social, environmental and economical factors, was considered through the options of multi-criteria optimization. A first part of this research was introduced in earlier research work explaining the general Bioenergy Decision System gBEDS [Ayoub N, Martins R, Wang K, Seki H, Naka Y. Two levels decision system for efficient planning and implementation of bioenergy production. Energy Convers Manage 2007;48:709–23]. In this paper, brief introduction and emphasize on gBEDS are given; the optimization model is presented and followed by a case study on designing a supply chain of nine bio-resources at Iida city in the middle part of Japan.     

Efficiency optimisation of the thermal energy storage unit in the form of the ceiling panel for summer conditions

The thermal energy storage unit in the form of the ceiling panel made of the gypsum‐microencapsulated phase change material composite with internal U‐shaped channels was optimised applying previously developed 1D‐3D numerical simulator. Calculations were carried out for variable inlet velocities of air, different properties of the composite expressed by enthalpy‐temperature curves, and summer climatic conditions in Central Poland (Warsaw). Optimal working temperature ranges of the composite were determined. Moreover, studies showed that for Central Poland, composites with wide ranges of the working temperature are preferred due to significant daily variations of ambient temperature during the whole summer.     

Genetic algorithms for optimisation of chemical kinetics reaction mechanisms

The aim of this paper is to review the techniques that exist in the literature for finding the optimal values of the reaction rates coefficients for a given reaction mechanism. Although traditional gradient based methods are reviewed as well, the paper focuses on recently developed self-adaptive evolutionary algorithms that outperform classical methods. Unlike the traditional, gradient-based methods one of the most important characteristics of computational intelligence techniques, such as genetic algorithms (GA), is the effectiveness and robustness in coping with uncertainty, insufficient information and noise. In this approach minimum human effort and little insight into the details of the chemical mechanism is required to generate the optimal values for the reaction rate coefficients. The use of the GA inversion procedure is illustrated on chemical systems of various complexities which govern the combustion of hydrogen, methane and kerosene.     

Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design

Thermoeconomic analyses in thermal system design are always focused on the economic objective. However, knowledge of only the economic minimum may not be sufficient in the decision making process, since solutions with a higher thermodynamic efficiency, in spite of small increases in total costs, may result in much more interesting designs due to changes in energy market prices or in energy policies. This paper suggests how to perform a multi-objective optimization in order to find solutions that simultaneously satisfy exergetic and economic objectives. This corresponds to a search for the set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by an evolutionary algorithm, that features a new diversity preserving mechanism using as a test case the well-known CGAM problem.     

Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms

Stirling engine has become preferable for high attention towards the use of alternate renewable energy resources like biomass and solar energy. Stirling engine is the main component of dish Stirling system in thermal power generation sector. Stirling engine is an externally heating engine, which theoretical efficiency is as high as Carnot cycle's, but actual ones are always far below compared with the Carnot efficiency. A number of studies have been done on multi-objective optimization to improve the design of Stirling engine. In the current study, a multi-objective optimization method, which is a combination of multiple optimization algorithms including differential evolution, genetic algorithm and adaptive simulated annealing, was proposed. This method is an attempt to generalize and improve the robustness and diversity with above three kinds of population based meta-heuristic optimization techniques. The analogous interpreter was linked and interchanged to find the best global optimal solution for Stirling engine performance optimization. It decreases the chance of convergence at a local minimum by powering from the fact that these three algorithms run parallel and members from each population and technique are swapped. The optimization considers five decision variables, including engine frequency, mean effective pressure, temperature of heating source, number of wires in regenerator matrix, and the wire diameter of regenerator, as multiple objectives. The Pareto optimal frontier was obtained and a final optimal solution was also selected by using various multi-criteria decision making methods including techniques for Order of Preference by Similarity to Ideal Solution and Simple Additive Weighting. The multi-objective optimization indicated a way for GPU-3 Stirling engine to obtain an output power of more than 3 kW and an increase by 5% in thermal efficiency with significant decrease in power loss due to flow resistance.     

Genetic algorithm-based strategy for the steam reformer optimization

The steam reforming reaction is widely used for obtaining hydrogen. The reforming reaction has a strong endothermic character, which means it requires a considerable and continuous heat supply to proceed. Due to the process character, a highly non-uniform temperature field develops inside the reactor. It has a consequence in large temperature gradients, leading to the catalyst degradation and a reduced lifetime of the reforming unit. The aim of the presented research is to unify the temperature field developing in the reactor, for easier control of the process and extension of the reformer's life expectancy. A conventional plug-flow reactor consists of a cylindrical pipe body filled with catalyst. The presented methodology included optimizing the catalyst distribution in the reactor to acquire the most uniform temperature field possible. A genetic algorithm is selected as an optimization technique for finding the most advantageous alignment of the catalyst. It is an example of evolutionary algorithms, basing on rules similar to natural selection. The algorithm generates a random, initial population of reactors and the reforming simulation is executed for each of them. The computation results are then evaluated and ranked using predefined fitness functions. The ranked reactors parameters' are further recombined with selection probability based on the fitness values, until a whole new population is created and the algorithm's loop restarts. The higher the fitness value of a specific reactor, the higher are the chances of passing its segments composition to the proceeding generation. The fitness computation leaves a vast space for improvements, as it may be computed based on many different process' parameters. This work focuses on distinguishing differences in the algorithm performance, depending on the formula for fitness calculation. The algorithm's converging speed, overall fitness values of specimens and optimization results were investigated and compared. The results show that the algorithm with an updated fitness calculation procedure performs considerably better. Only about 50% of computational time was required, to acquire results of the same quality for the presented numerical cases, when comparing with the previously prepared procedure.     

Comparison of radiosity and ray-tracing techniques with a practical design procedure for the prediction of daylight levels in atria

Designers are often facing prescribed requirements concerning daylight in atria. For the accurate prediction of the illuminances the designers should employ either computer simulations or apply empirical equations. This study compares results obtained by a practical design procedure and simulated results using Radiance and Lightscape 3.2.     

基于多目标遗传算法的生物质气化过程参数优化

生物质气化过程是一个复杂的多目标非线性过程。通过对气化过程的机理分析,针对麦秸和玉米秸这2种软质秸秆类生物质原料特性,建立了气化过程的优化目标函数。在此基础上,采用多目标遗传算法对该目标函数进行优化设计计算。计算结果表明,该目标函数对生物质气化过程参数优化具有良好效果,也验证了该算法对于全局优化以及解决复杂非线性问题的有效性。     

Design and techno-economical optimization for stand-alone hybrid power systems with multi-objective evolutionary algorithms

The optimal design of the hybrid energy system can significantly improve the economical and technical performance of power supply. However, the problem is formidable because of the uncertain renewable energy supplies, the uncertain load demand, the nonlinear characteristics of some components, and the conflicting techno-economical objectives. In this work, the optimal design of the hybrid energy system has been formulated as a multi-objective optimization problem. We optimize the techno-economical performance of the hybrid energy system and analyse the trade-offs between the multi-objectives using multi-objective genetic algorithms. The proposed method is tested on the widely researched hybrid PV-wind power system design problem. The optimization seeks the compromise system configurations with reference to three incommensurable techno-economical criteria, and uses an hourly time-step simulation procedure to determine the design criteria with the weather resources and the load demand for one reference year. The well-known efficient multi-objective genetic algorithm, called NGAS-II (the fast elitist non-dominated sorting genetic algorithm), is applied on this problem. A hybrid PV-wind power system has been designed with this method and several methods in the literature. The numerical results demonstrate that the proposed method is superior to the other methods. It can handle the optimal design of the hybrid energy system effectively and facilitate the designer with a range of the design solutions and the trade-off information. For this particular application, the hybrid PV-wind power system using more solar panels achieves better technical performance while the one using more wind power is more economical. Copyright © 2006 John Wiley & Sons, Ltd.     

Discomfort glare assessment and prevention for daylight applications in office environments

Discomfort glare from daylight is a common problem in many office environments. It has been the subject of a significant body of research and many attempts have been made to develop reliable assessment and prediction models to address this problem. This paper reviews and discusses the advantages and limitations of using existing glare indices for daylighting conditions. It concludes that available assessment and prediction methods are of limited practical use in daylit situations and currently have no provision for integrated systems that combine daylighting and electric lighting. The paper also presents selected findings from a case study of daylit office environments which identify a number of important design considerations. On the basis of the discussion and the findings from the case study, additional research needs are identified to overcome current limitations. In the meantime, until results from such research are available and can be practically applied, this paper suggests ways to better integrate computer workstations in daylit offices.     

Genetic algorithms and non-intrusive energy management system based economic dispatch for cogeneration units

By integrating neural networks (NNs) with turn-on transient energy analysis, this work attempts to recognize demand load, including the buyers’ load on the power systems and the internal load on the cogeneration systems, thereby increasing the recognition accuracy in a non-intrusive energy management (NIEM) system. Analysis results reveal that an NIEM system and a new method that is based on genetic algorithms (GA) can effectively manage energy demand in an optimal economic dispatch for cogeneration systems with multiple cogenerators, which generate power for buyers. Furthermore, the global optimum of economic dispatch under typical environmental and operating constraints of cogeneration systems is found using the proposed approach, which is based on genetic algorithms. Moreover, the use of the proposed GA-based method for economic dispatch can substantially reduce computational time, fuel cost, power cost and air pollution.     

Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system

This paper presents an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and irradiance conditions. First, for the purpose of comparison and because of its proven and good performances, the perturbation and observation (P&O) technique is briefly introduced. A fuzzy logic controller based MPPT (FLC) is then proposed which has shown better performances compared to the P&O MPPT based approach. The proposed FLC has been also improved using genetic algorithms (GA) for optimisation. Different development stages are presented and the optimized fuzzy logic MPPT controller (OFLC) is then simulated and evaluated, which has shown better performances.     

Automated demand response system in lighting for optimization of electricity cost

Daylighting is an important factor in improving visual comfort and energy efficiency. Lighting control using daylighting can reduce energy consumption in buildings. This thesis proposes an automatic demand response system for lighting based on wireless sensor networks (WSN) in order to reduce the peak electricity demand according to the stage of electricity rate with real-time pricing (RTP), time of use pricing (TOUP), and critical peak pricing (CPP). The proposed system automatically controls the slat angle of the venetian blind with a cut-off angle according to the altitude of the sun, automatically executing light dimming according to measured current luminance to remove an unpleasant glare caused by daylighting. The target illuminance of area at this time is set at a minimum illuminance required for the work execution in the office during the time zone where the electricity load is high to save the lighting energy cost, while a maximum illuminance is set during the time zone where the electricity load is low according to the real-time electricity pricing stages obtained through the advanced metering infrastructure (AMI) in order to improve the work efficiency of the occupants. In this study, two testbeds having the same environments, as well as a control system targeting a fixed illuminance per price system, were established. The illuminance energy consumption and cost were then measured and the effect of the proposed illuminance system was evaluated.     

Geometric optimization of thermoelectric coolers in a confined volume using genetic algorithms

The demand for thermoelectric coolers (TEC) has grown significantly because of the need for a steady, low-temperature operating environment for various electronic devices such as laser diodes, semiconductor equipment, infrared detectors and others. The cooling capacity and its coefficient of performance (COP) are both extremely important in considering applications. Optimizing the dimensions of the TEC legs provides the advantage of increasing the cooling capacity, while simultaneously considering its minimum COP. This study proposed a method of optimizing the dimensions of the TEC legs using genetic algorithms (GAs), to maximize the cooling capacity. A confined volume in which the TEC can be placed and the technological limitation in manufacturing a TEC leg were considered, and three parameters––leg length, leg area and the number of legs––were taken as the variables to be optimized. The constraints of minimum COP and maximum cost of the material were set, and a genetic search was performed to determine the optimal dimensions of the TEC legs. This work reveals that optimizing the dimensions of the TEC can increase its cooling capacity. The results also show that GAs can determine the optimal dimensions according to various input currents and various cold-side operating temperatures.     

New approach relevant to total heat transfer coefficient including the effect of radiation and convection at the ceiling in a cooled ceiling room

In this study, radiative and convective heat transfer coefficients at the ceiling are determined for a cooled ceiling room. Firstly, convective heat transfer is simulated numerically neglecting the radiative heat transfer at the surfaces (ε_f = ε_w = ε_c = 0), then, radiative heat transfer is calculated theoretically for different surface emissivities (ε_f = ε_w = ε_c = 0.5, 0.6, 0.7, 0.8 and 0.9) for different room dimensions (3 × 3 × 3, 4 × 3 × 4 and 6 × 3 × 4 m) and thermal conditions (T_f = 25 °C, T_w = 28–36 °C and T_c = 0–25 °C). Numerical data is compared with the results of correlations based on experimental data given in literature. New equations related to convective and total (including the effect of convection and radiation) heat transfer coefficients for ceiling are found in the current study.